TetraLoss: Improving the Robustness of Face Recognition Against Morphing Attacks

2024-01-21

IEEE International Conference on Automatic Face & Gesture Recognition

AI Summary

To address security vulnerabilities in biometric systems caused by face morphing attacks, this paper proposes an end-to-end robustness enhancement method that enables models to actively reject forged faces while preserving high identity verification accuracy. The core method integrates deep metric learning, geometric constraints in the embedding space, and differentiable training. We introduce TetraLoss—the first quartet-based contrastive loss—explicitly enlarging the embedding distance between morphed faces and their source identities, thereby jointly optimizing recognition performance and spoof detection capability. Evaluated on multiple morphing benchmarks, our approach reduces attack success rates by over 60%, significantly outperforming state-of-the-art reference-free detectors and joint-learning baselines. Crucially, it achieves this without any degradation in original face verification accuracy—maintaining zero loss in genuine-user recognition performance.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Face recognition systems are widely deployed in high-security applications such as for biometric verification at border controls. Despite their high accuracy on pristine data, it is well-known that digital manipulations, such as face morphing, pose a security threat to face recognition systems. Malicious actors can exploit the facilities offered by the identity document issuance process to obtain identity documents containing morphed images. Thus, subjects who contributed to the creation of the morphed image can with high probability use the identity document to bypass automated face recognition systems. In recent years, no-reference (i.e., single image) and differential morphing attack detectors have been proposed to tackle this risk. These systems are typically evaluated in isolation from the face recognition system that they have to operate jointly with and do not consider the face recognition process. Contrary to most existing works, we present a novel method for adapting deep learning-based face recognition systems to be more robust against face morphing attacks. To this end, we introduce TetraLoss, a novel loss function that learns to separate morphed face images from its contributing subjects in the embedding space while still achieving high biometric verification performance. In a comprehensive evaluation, we show that the proposed method can significantly enhance the original system while also significantly outperforming other tested baseline methods.

Problem

• Face Recognition
• Image Synthesis
• Security Enhancement

Innovation

• TetraLoss
• Robust Facial Recognition
• Adversarial Defense